Automation of a robotised production cell

This article presents a solution to the challenge of developing a new test bench design to test power meters with the flexibility to interoperate with automation devices and robotics arms through industrial protocols and adaptability to various product lines.

Sagemcom Technology, an NI Alliance Partner located in Tunisia, was awarded a project to increase production volume in a Tunisia factory and ensure a contractual engagement with a customer for line fluidity equal to 99%. Previously, the testing solutions were based on industrial personal computer architecture, were manually operated, and consisted of the following stations:

Vision inspection on product’s LCD display.

High-potential test (HIPOT).

Powerline communication test (PLC).

Functional test.

During the vision inspection, the functionality of the LCD and indicating LED of the product needed to be guaranteed. In the past, operators performed tests through manual inspection. This was automated through PC-based automatic test benches using Vision Builder for Automated Inspection. This test is performed through interfacing with cameras, automation devices, products through Ethernet communication, and safety controllers through Modbus TCP/IP.

Concerning the HIPOT test, 3,2 kV was injected into a product to check its immunity to this high voltage. The accompanying instrumentation needed to be interfaced through RS232 interface and a precise time frame was used for the leakage current’s measurement. The purpose of the PLC test is to simulate the behaviour of the product after field deployment while communicating with the data concentrator in the powerline protocol. Ethernet/IP communication was used with a special instrument for signal modulation analysis. During functional testing, communication signals have to be acquired and analysed, followed by interaction with a product’s button while acting with a cylinder. This test is quite sensitive due to the accuracy needed to interact with some buttons that have limited access.

Fig. 1: Robot cell concept design.

Duplicating this architecture does not ensure contractual first pass yield (FPY) engagement due to the troubles observed with PC-based solutions (crashes, application bugs, and virus vulnerability). In addition, a budgetary improvement request was introduced to limit investment on development budget and global solution costs. These criteria presented a challenge and a constructive way forward was needed in order to:

Enhance FPY and robustness of test benches.

Reduce development time and save money.

Reduce test time and increase volume to save on our investment.

Reduce handling time to provide a better throughput and avoid operator-related delays.

Add technical value to the project.

Reducing handling time and providing better throughput

After considering the challenge, the first step was to determine an efficient way to reduce handling time and deliver better accuracy while positioning products into test fixtures. Simulations concluded that a robotic cell configuration with a central robot for handling products and an additional smaller robot for performing functional tests would work well. Four test benches were interconnected and synchronised to ensure optimal process flow. Even though robots were selected for better handling time, there was an additional challenge of finding the best way to program them for an efficient predictive algorithm and setting priority to robot handling.

Increased robustness and reduced testing time

The objective of reducing test time without neglecting robustness significantly impacted on the challenge. Through this condition the team had to identify the most suitable hardware architecture that could provide the highest level of robustness for each cell’s node. In fact, the project income was to guarantee that the adopted architecture could permit safe data exchange between each node in a way that the product could be functionally tested and inspected through vision. A necessary algorithm had to be provided to set the priority for positioning the robot arms while communicating through DeviceNet protocol and supervising all safety sensors to prevent any security violation. After performing primary studies, the team identified a large number of digital I/O and a variety of instruments that communicate through RS422 protocol, Ethernet/IP, Modbus TCP/IP, and even DeviceNet. Thus, there needed to be a control platform that could:

After analysing the available solutions for machine control, focus was turned to two possible compliant architectures: either using programmable logic controller platforms, or the CompactRIO system. The latter was chosen because of the calculation power of its processor and FPGA, its support of a large number of industrial protocols, and its easy interoperability with a system design and visual programming platform. The system offers built-in vision capabilities and supports camera connectivity over USB and gigabit Ethernet, which are key differentiators. This system leads to a fully integrated solution and cost-savings by avoiding costly smart camera solutions coupled to programmable logic controllers. The platform can also accelerate embedded vision applications through the vision development module, which includes many image processing functions that can run on both a real-time processor and an FPGA. The solution offered a way to reduce test time by paralysing acquisitions and test sequence.

Fig. 2: View of the robotic testing cell.

The cRIO-9030 was used for this project due to its powerful dual-core Intel Atom E3825, its high-value Kintex-7 FPGA, and the possibility of handling an embedded user interface through its MiniDisplay port, which could help save money by using additional PCs for deporting HMI. The system assisted in overcoming a technical issue observed in the PC-based architecture. A sensible burst measurement can cause a high level of false failure. Even with a costly digital multimeter deployed into that previous architecture, there was a high level of line rejection. The team had to perform an RMS measurement accurately for a burst signal having an average amplitude of 700 mV modulated at 50 KHz. This burst allowed the meter to synchronise with the powerline communication network.

The measurement tolerance was tiny at 25 mV, and there was a very short amount of time to start the measurement, 10 µs trigger, which explained the sensitivity of the test.
When using the FPGA, the CompactRIO processing performance could attain an accurate 10 µs time loop for ensuring synchronisation with the burst signal trigger, which offered a significant advantage in terms of test time and rejection rate compared to the previous PC and digital multimeter solution. With the previous solution the burst had to be initiated several times and measure for a long timeframe before catching the trigger and ensuring the measurement. Thus, the team reached about 4 S instead of 10 µs. Those 10 µs were guaranteed when using CompactRIO because of the platform’s large number of available data acquisition modules.

Reducing development time and saving investment

The team felt confident that LabVIEW would suit this project. In the beginning there were concerns that the migration and upgrade of actual source code from the PC would take additional development time and resources.

Fortunately, due to the inherent scalability of LabVIEW, the team reused as much as 70% of its code when changing the hardware platform from PC to CompactRIO with minimal coding effort, and saved a significant amount of development time and cost. The state machine architecture suited the project needs, and the ability of LabVIEW to natively handle the multi-threading programming was crucial in reducing development time and avoiding multi-threading programming challenges previously faced with sequencing programming languages including C/C++ and LabWindows/CVI software. The large set of control and mathematics libraries available in LabVIEW provided great support for developing the predictive algorithm to evaluate robot positioning. LabVIEW also offered support for industrial communication protocols for robotic interfacing like DeviceNet with the release of industrial communications for DeviceNet 15, which saved on man-hours.

Project outcome

Due to the high interoperability of the CompactRIO system and the native support for true parallelism and precise time looping through FPGA, test time was reduced by 21 s, which led to 17% productivity growth. This represents a higher throughput and a significant return on investment. The number of controllers was also reduced by using cRIO-9030 for all test benches instead of a single industrial PC for each machine, as had been done previously. This saved 50% on controller cost.

Compared to the Windows-based vision inspection solution using Vision Builder for Automated Inspection, the CompactRIO system accelerated test time by almost 38%. Vision Inspection runs much faster on this system than Vision Builder for Automated Inspection does on Windows.

CompactRIO also enhanced the robustness of the benches and made maintenance easier. Prior to introducing this system, the team had to manually check with the digital multimeter on contact quality between test pin and product to evaluate impact on current measurement. Voltage drop can be simultaneously acquired on all contacting points between pins and products and the contacting quality can be remotely monitored, which guarantees higher measurement accuracy and helps the maintenance team proactively predict and prevent pin failures before they occur.

Despite that, the automation solution based on the CompactRIO platform and robotic arms has reduced the number of operators in the production line. It has provided a cost savings compromise that was used to add technical value to the project and to hire more qualified support engineers. Hence the solution balanced the pure technological challenge with a responsible involvement for improvement and recruitment.

Conclusion and future outlook

In conclusion, this new architecture of functional testing cells represented an important technological achievement. Sagemcom plans many optimisation perspectives to take the maximum advantage of the FPGA image coprocessing capabilities by offloading the processor from intensive portions of the vision application. This frees the processor to handle other tasks and can lead to additional test time savings. This should be achieved easily considering that the vision development module includes more than 50 FPGA image processing functions to efficiently handle the image transfer between the processor and FPGA. The vision capabilities of the CompactRIO platform offered additional perspectives for developing robotics guidance using LabVIEW FPGA IP Builder, which provides autonomous, precise positioning for robots.